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AI Agent 从入门到实战：概念理解、MCP 使用、平台实操、工作流自动化
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LangGraph Complete Guide 2026: Build Stateful AI Agents
Build agents with cycles, memory, human-in-the-loop using LangGraph
LangGraph 完全指南（2026）：把 Agent 建成状态机——State+reducer、条件边、checkpointer 持久化（多轮记忆/崩溃恢复/时间旅行）、interrupt 人工审批门、多 Agent supervisor 模式。含何时该用/不该用的诚实对照。
LlamaIndex Tutorial 2026: Build Production RAG Applications
Connect LLMs to your documents with LlamaIndex ingestion pipelines and query engines
Complete LlamaIndex tutorial 2026. Covers VectorStoreIndex, persistent Qdrant storage, chat engines, sub-question decomposition, semantic chunking, metadata filtering, and streaming.
DSPy Tutorial 2026: Automatic LLM Prompt Optimization
Replace manual prompt engineering with DSPy automatic optimization
Complete DSPy tutorial. Covers typed signatures, chain-of-thought reasoning, building RAG pipelines, and automatic optimization with MIPROv2 using training examples and metrics.
Build a Full-Stack AI SaaS App with Next.js 16, Clerk, and Supabase 2026
Step-by-step guide to building a production-ready AI SaaS application with authentication, usage limits, subscription billing, and AI features
Complete tutorial for building a full-stack AI SaaS application using Next.js 16, Clerk for authentication, Supabase for database, and OpenAI for AI features. Covers user management, usage metering, stripe billing, and deploying to production.
Fine-Tuning LLMs with LoRA and QLoRA: Complete Guide 2026
Train custom AI models from Llama 3 and Mistral using LoRA/QLoRA fine-tuning on a single consumer GPU with less than 24GB VRAM
Complete guide to fine-tuning large language models using LoRA and QLoRA techniques in 2026. Covers dataset preparation, training configuration, hardware requirements, evaluation metrics, and deploying fine-tuned models to production.
Building a RAG System from Scratch: Complete Python Tutorial 2026
Build a production-quality Retrieval Augmented Generation system step by step, from document processing to API deployment
Complete hands-on tutorial for building a RAG (Retrieval Augmented Generation) system from scratch in Python. Covers document chunking, embedding generation, vector storage, retrieval optimization, reranking, and building a production API.